aif360.metrics.MDSSClassificationMetric

class aif360.metrics.MDSSClassificationMetric(dataset: BinaryLabelDataset, classified_dataset: BinaryLabelDataset, scoring: str | ScoringFunction = 'Bernoulli', unprivileged_groups: dict = None, privileged_groups: dict = None, **kwargs)

Bias subset scanning is proposed as a technique to identify bias in predictive models using subset scanning [1].

This class is a wrapper for the bias scan scoring and scanning methods that uses the ClassificationMetric abstraction.

References

[1]

Zhang, Z. and Neill, D. B., “Identifying significant predictive bias in classifiers,” arXiv preprint, 2016.

Parameters:

• dataset (BinaryLabelDataset) – Dataset containing ground-truth labels.

• classified_dataset (BinaryLabelDataset) – Dataset containing predictions.

• scoring (str or ScoringFunction) – One of ‘Bernoulli’ (parametric), or ‘BerkJones’ (non-parametric) or subclass of aif360.metrics.mdss.ScoringFunctions.ScoringFunction. Defaults to Bernoulli.

• privileged_groups (list(dict)) – Privileged groups. Format is a list of dicts where the keys are protected_attribute_names and the values are values in protected_attributes. Each dict element describes a single group. See examples for more details.

• unprivileged_groups (list(dict)) – Unprivileged groups in the same format as privileged_groups.

Methods

accuracy	\(ACC = (TP + TN)/(P + N)\).
average_abs_odds_difference	Average of absolute difference in FPR and TPR for unprivileged and privileged groups:
average_odds_difference	Average of difference in FPR and TPR for unprivileged and privileged groups:
average_predictive_value_difference	Average of difference in PPV and FOR for unprivileged and privileged groups:
base_rate	Compute the base rate, \(Pr(Y = 1) = P/(P+N)\), optionally conditioned on protected attributes.
between_all_groups_coefficient_of_variation	The between-group coefficient of variation is the square root of two times the between_all_groups_generalized_entropy_index() with \(\alpha = 2\).
between_all_groups_generalized_entropy_index	Between-group generalized entropy index that uses all combinations of groups based on self.dataset.protected_attributes.
between_all_groups_theil_index	The between-group Theil index is the between_all_groups_generalized_entropy_index() with \(\alpha = 1\).
between_group_coefficient_of_variation	The between-group coefficient of variation is the square root of two times the between_group_generalized_entropy_index() with \(\alpha = 2\).
between_group_generalized_entropy_index	Between-group generalized entropy index that uses self.privileged_groups and self.unprivileged_groups as the only two groups.
between_group_theil_index	The between-group Theil index is the between_group_generalized_entropy_index() with \(\alpha = 1\).
binary_confusion_matrix	Compute the number of true/false positives/negatives, optionally conditioned on protected attributes.
coefficient_of_variation	The coefficient of variation is the square root of two times the generalized_entropy_index() with \(\alpha = 2\).
consistency	Individual fairness metric from [1]_ that measures how similar the labels are for similar instances.
difference	Compute difference of the metric for unprivileged and privileged groups.
differential_fairness_bias_amplification	Bias amplification is the difference in smoothed EDF between the classifier and the original dataset.
disparate_impact
equal_opportunity_difference	Alias of true_positive_rate_difference().
equalized_odds_difference	Greater of the absolute difference in FPR and TPR for unprivileged and privileged groups
error_rate	\(ERR = (FP + FN)/(P + N)\)
error_rate_difference	Difference in error rates for unprivileged and privileged groups, \(ERR_{D = \text{unprivileged}} - ERR_{D = \text{privileged}}\).
error_rate_ratio	Ratio of error rates for unprivileged and privileged groups, \(\frac{ERR_{D = \text{unprivileged}}}{ERR_{D = \text{privileged}}}\).
false_discovery_rate	\(FDR = FP/(TP + FP)\)
false_discovery_rate_difference	\(FDR_{D = \text{unprivileged}} - FDR_{D = \text{privileged}}\)
false_discovery_rate_ratio	\(\frac{FDR_{D = \text{unprivileged}}}{FDR_{D = \text{privileged}}}\)
false_negative_rate	\(FNR = FN/P\)
false_negative_rate_difference	\(FNR_{D = \text{unprivileged}} - FNR_{D = \text{privileged}}\)
false_negative_rate_ratio	\(\frac{FNR_{D = \text{unprivileged}}}{FNR_{D = \text{privileged}}}\)
false_omission_rate	\(FOR = FN/(TN + FN)\)
false_omission_rate_difference	\(FOR_{D = \text{unprivileged}} - FOR_{D = \text{privileged}}\)
false_omission_rate_ratio	\(\frac{FOR_{D = \text{unprivileged}}}{FOR_{D = \text{privileged}}}\)
false_positive_rate	\(FPR = FP/N\)
false_positive_rate_difference	\(FPR_{D = \text{unprivileged}} - FPR_{D = \text{privileged}}\)
false_positive_rate_ratio	\(\frac{FPR_{D = \text{unprivileged}}}{FPR_{D = \text{privileged}}}\)
generalized_binary_confusion_matrix	Compute the number of generalized true/false positives/negatives, optionally conditioned on protected attributes.
generalized_entropy_index	Generalized entropy index is proposed as a unified individual and group fairness measure in [3]_.
generalized_equalized_odds_difference	Greater of the absolute difference in generalized FPR and generalized TPR for unprivileged and privileged groups
generalized_false_negative_rate	\(GFNR = GFN/P\)
generalized_false_positive_rate	\(GFPR = GFP/N\)
generalized_true_negative_rate	\(GTNR = GTN/N\)
generalized_true_positive_rate	Return the ratio of generalized true positives to positive examples in the dataset, \(GTPR = GTP/P\), optionally conditioned on protected attributes.
mean_difference	Alias of statistical_parity_difference().
negative_predictive_value	\(NPV = TN/(TN + FN)\)
num_false_negatives	\(FN = \sum_{i=1}^n \mathbb{1}[y_i = \text{favorable}]\mathbb{1}[\hat{y}_i = \text{unfavorable}]\)
num_false_positives	\(FP = \sum_{i=1}^n \mathbb{1}[y_i = \text{unfavorable}]\mathbb{1}[\hat{y}_i = \text{favorable}]\)
num_generalized_false_negatives	Return the generalized number of false negatives, \(GFN\), the weighted sum of 1 - predicted scores where true labels are 'favorable', optionally conditioned on protected attributes.
num_generalized_false_positives	Return the generalized number of false positives, \(GFP\), the weighted sum of predicted scores where true labels are 'unfavorable', optionally conditioned on protected attributes.
num_generalized_true_negatives	Return the generalized number of true negatives, \(GTN\), the weighted sum of 1 - predicted scores where true labels are 'unfavorable', optionally conditioned on protected attributes.
num_generalized_true_positives	Return the generalized number of true positives, \(GTP\), the weighted sum of predicted scores where true labels are 'favorable', optionally conditioned on protected attributes.
num_instances	Compute the number of instances, \(n\), in the dataset conditioned on protected attributes if necessary.
num_negatives	Compute the number of negatives, \(N = \sum_{i=1}^n \mathbb{1}[y_i = 0]\), optionally conditioned on protected attributes.
num_positives	Compute the number of positives, \(P = \sum_{i=1}^n \mathbb{1}[y_i = 1]\), optionally conditioned on protected attributes.
num_pred_negatives	\(\sum_{i=1}^n \mathbb{1}[\hat{y}_i = \text{unfavorable}]\)
num_pred_positives	\(\sum_{i=1}^n \mathbb{1}[\hat{y}_i = \text{favorable}]\)
num_true_negatives	\(TN = \sum_{i=1}^n \mathbb{1}[y_i = \text{unfavorable}]\mathbb{1}[\hat{y}_i = \text{unfavorable}]\)
num_true_positives	Return the number of instances in the dataset where both the predicted and true labels are 'favorable', \(TP = \sum_{i=1}^n \mathbb{1}[y_i = \text{favorable}]\mathbb{1}[\hat{y}_i = \text{favorable}]\), optionally conditioned on protected attributes.
performance_measures	Compute various performance measures on the dataset, optionally conditioned on protected attributes.
positive_predictive_value	\(PPV = TP/(TP + FP)\)
power	Alias of num_true_positives().
precision	Alias of positive_predictive_value().
ratio	Compute ratio of the metric for unprivileged and privileged groups.
recall	Alias of true_positive_rate().
rich_subgroup	Audit dataset with respect to rich subgroups defined by linear thresholds of sensitive attributes
score_groups	Compute the bias score for a prespecified group of records.
selection_rate	\(Pr(\hat{Y} = \text{favorable})\)
sensitivity	Alias of true_positive_rate().
smoothed_empirical_differential_fairness	Smoothed EDF from [#foulds18]_.
specificity	Alias of true_negative_rate().
statistical_parity_difference
theil_index	The Theil index is the generalized_entropy_index() with \(\alpha = 1\).
true_negative_rate	\(TNR = TN/N\)
true_positive_rate	Return the ratio of true positives to positive examples in the dataset, \(TPR = TP/P\), optionally conditioned on protected attributes.
true_positive_rate_difference	\(TPR_{D = \text{unprivileged}} - TPR_{D = \text{privileged}}\)

__init__(dataset: BinaryLabelDataset, classified_dataset: BinaryLabelDataset, scoring: str | ScoringFunction = 'Bernoulli', unprivileged_groups: dict = None, privileged_groups: dict = None, **kwargs)

Parameters:

• dataset (BinaryLabelDataset) – Dataset containing ground-truth labels.

• classified_dataset (BinaryLabelDataset) – Dataset containing predictions.

• scoring (str or ScoringFunction) – One of ‘Bernoulli’ (parametric), or ‘BerkJones’ (non-parametric) or subclass of aif360.metrics.mdss.ScoringFunctions.ScoringFunction. Defaults to Bernoulli.

• privileged_groups (list(dict)) – Privileged groups. Format is a list of dicts where the keys are protected_attribute_names and the values are values in protected_attributes. Each dict element describes a single group. See examples for more details.

• unprivileged_groups (list(dict)) – Unprivileged groups in the same format as privileged_groups.

score_groups(privileged=True, penalty=1e-17)

Compute the bias score for a prespecified group of records.

Parameters:

privileged (bool) – Flag for which direction to scan: privileged (True) implies negative (observed worse than predicted outcomes) while unprivileged (False) implies positive (observed better than predicted outcomes).

Returns:

float –

Bias score for the given group.

The higher the score, the evidence for bias.